A Bibliometric Analysis of the Application of Social Network Analysis in Supply Chain Management

• Autorzy: Can Wang
• Języki publikacji: EN

Abstrakty

EN

Background: This paper presents a bibliometric overview of research published application of social network analysis in supply chain management in recent decades. It may be useful for showing the most important problems in this area. With this aim, Citespace is used to analyse the literature on the application of social network analysis in supply chain management to clarify the development and research trend. Bibliometric analysis is the quantitative study of bibliographic material. It provides a general picture of a research field that can be classified by papers, authors, and journals. The main objective of this study is to investigate the knowledge domain about application social network analysis in the supply chain field and reveal the thematic patterns and topics of high interest to researchers to predict emerging trends in the literature. Methods: To investigate the growth of studies about the applicable social network in supply chain management, 647 articles were reviewed by CiteSpace software. These papers were collected from the Core Collection of Thomson Reuters and published in 16 journals in operations research and management science from 2004 to 2021. Document co-citation analysis, clustering analysis, and citation burst detection were conducted to investigate and examine the thematic patterns, emerging trends, and critical articles of the knowledge domain. Results: Social network approaches are increasingly popular in the supply chain. Four major clusters are discussed in detail, namely multi-objective optimization, sustainable supply chain, supply network, and circular economy. Three research trends of supply chain network design, structural characteristics, and supplier selection and evaluation were identified based on citation bursts analysis. Conclusions: The present study offers a new approach to visualizing relevant data to synthesize scientific research findings of the application of social network analysis in supply chain management. Additionally, directions for future research in this area are presented.

Słowa kluczowe

PL

Łańcuch dostaw; Analiza sieci społecznych; Analiza bibliometryczna

EN

Supply chain; Social Network Analysis (SNA); Bibliometric analysis

• Czasopismo: LogForum
• Rocznik: 2022
• Tom: 18
• Numer: nr 1
• Strony: 123-136

Twórcy

autor

Can Wang

• Zhongnan University of Economics and Law, China

Bibliografia

• Alinaghian L., Qiu J., Razmdoost K., 2Q2Q. The role of network structural properties in supply chain sustainability: a systematic literature review and agenda for future research. Supply Chain Management: An International Journal, 26(2): 192-211. https://doi.org/1Q.11Q8/SCM-11-2Q19-Q4Q7
• Bellamy M.A., Ghosh S., Hora M., 2Q14. The influence of supply network structure on firm innovation. Journal of Operations Management, 32(6): 357-373. https://doi.org/1Q.1Q16/j.jom.2Q14.Q6.QQ4
• Bing L., 2Q11. Social Network Analysis. Springer Berlin Heidelberg.
• Bode C., Wagner S.M., 2Q15. Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions. Journal of Operations Management, 36: 215228. https://doi.org/1Q.1Q16/j.jom.2Q14.12.QQ4
• Borgatti S., Li X., 2QQ9. On social network analysis in a supply chain context. Social Science Electronic Publishing, 45(2): 5-22. https://doi.org/1Q.1111/i.1745- 493X2009,03166
• Brandenburg M., Govindan K., Sarkis J., Seuring S., 2Q14. Quantitative models for sustainable supply chain management: developments and directions. European Journal of Operational Research, 233(2): 299-312. https://doi.org/1Q.1Q16/j.ejor.2Q13.Q9.Q32
• Carter C.R., Rogers D.S., Choi T.Y., 2Q15. Toward the theory of the supply chain. Journal of Supply Chain Management, 51(2): 89-97. https://doi.org/10.1111/jscm. 12073
• Chaabane A., Ramudhin A., Paquet M., 2Q12. Design of sustainable supply chains under the emission trading scheme. International Journal of Production Economics, 135(1): 37-49. https://doi.org/1Q.1Q16/j.ijpe.2Q1Q.1Q.Q25
• Chen C., 2004. Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National Academy of Sciences of the United States of America, 101: 5303-5310. https://doi.org/10.1073/pnas.0307513100
• Chen C., 2006. CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3): 359-377. https://doi.org/10.1002/asi.20317
• Chen C., 2017. Science Mapping: A Systematic Review of the Literature. Journal of Data and Information Science, 2(2): 1-40. https://doi.org/10.1515/jdis-2017-0006
• Choi T.Y., Kim Y., 2010. Structural embeddedness and supplier management: a network perspective. Journal of Supply Chain Management, 44(4): 5-13. https://doi.org/10.1111/j.1745- 493X.2008.00069.x
• Choi T.Y., Wu Z.H., 2009. Triads in supply networks: theorizing buyer-supplier-supplier relationships. Journal of Supply Chain Management, 45(1): 8-25. https://doi.org/10.1111/j.1745-93X.2009.03151.x
• Devika K., Jafarian A., Nourbakhsh V., 2014. Designing a sustainable closed-loop supply chain network based on triple bottom line approach: a comparison of metaheuristics hybridization techniques. European Journal of Operational Research, 235(3): 594-615. https://doi.org/10.1016/j.ejor.2013.12.032
• Eskandarpour M., Dejax P., Miemczyk J., Peton O., 2015. Sustainable supply chain network design: an optimization-oriented review. Omega, 54: 11-32. https://doi.org/10.1016/j.omega.2015.01.00 6
• Fahimnia B., Sarkis J., Davarzani H., 2015. Green supply chain management: a review and bibliometric analysis. International Journal of Production Economics, 162: 101114. https://doi.org/10.1016/j.ijpe.2015.01.003
• Fursov K.S., Kadyrova A.R., 2017. How the analysis of transitionary references in knowledge networks and their centrality characteristics helps in understanding the genesis of growing technology areas. Scientometrics, 111(3): 1947-1963. https://doi.org/10.1007/s11192-017-2340-z
• Galaskiewicz J., 2011. Studying supply chains from a social network perspective. Journal of Supply Chain Management, 47(1): 4-8. https://doi.org/10.1111/j.1745- 493X.2010.03209.x
• Govindan K., Soleimani H., Kannan D., 2015. Reverse logistics and closed-loop supply chain: a comprehensive review to explore the future. European Journal of Operational Research, 240(3): 603-626. https://doi.org/10.1016/j.ejor.2014.07.012
• Govindan K., Khodaverdi R., Jafarian A., 2013. A fuzzy multi-criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. Journal of Cleaner Production, 47: 345-354. https://doi.org/10.1016/j.jclepro.2012.04.01 4
• Govindan K., Rajendran S., Sarkis J., Murugesan P., 2015. Multi-criteria decision-making approaches for green supplier evaluation and selection: a literature review. Journal of Cleaner Production, 98: 66-83. https://doi.org/10.1016/j.jclepro.2013.06.04 6
• Grimm J.H., Hofstetter J.S., Sarkis J., 2014. Critical factors for sub-supplier management: a sustainable food supply chains perspective. International Journal of Production Economics, 152: 159-173. https://doi.org/10.1016/j.ijpe.2013.12.011
• Han Y., Caldwell N.D., Ghadge A., 2020. Social network analysis in operations and supply chain management: a review and revised research agenda. International Journal of Operations & Production Management, 40(7/8): 1153-1176. https://doi.org/10.1108/IJOPM-06-2019- 0500
• Hassini E., Surti C., Searcy C., 2012. A literature review and a case study of sustainable supply chains with a focus on metrics. International Journal of Production Economics, 140(1): 69-82. https://doi.org/10.1016/j.ijpe.2012.01.042
• Kim Y., Choi T.Y., Yan T., Dooley K., 2011. Structural investigation of supply networks: A social network analysis approach. Journal of Operations Management, 29(3): 194-211. https://doi.org/10.1016/j.jom.2010.11.001
• Leydesdorff L., Wagner C.S., Bornmann L., 2018. Betweenness and Diversity in Journal Citation Networks as Measures of Interdisciplinarity-A Tribute to Eugene Garfield. Scientometrics, 114 (2): 567-592. https://doi.org/10.1007/s11192-017-2528-2
• Li Y., Li H., Liu N., Liu X., 2018. Important institutions of interinstitutional scientific collaboration networks in materials science. Scientometrics, 117(1): 85-103. https://doi.org/10.1007/s11192-018-2837-0
• Liu N., Wang J., Song Y., 2019. Organization mechanisms and spatial characteristics of urban collaborative innovation networks: a case study in Hangzhou, China. Sustainability, 11(21): 5988. https://doi.org/10.3390/su11215988
• Mota B., Gomes M. I., Carvalho A., Barbosa-Povoa A. P., 2015. Towards supply chain sustainability: economic, environmental and social design and planning. Journal of Cleaner Production, 105: 14-27. https://doi.org/10.1016/j.jclepro.2014.07.05 2
• Pathak S.D., Day J.M., Nair A., Sawaya W.J., Kristal M.M., 2010. Complexity and adaptivity in supply networks: building supply network theory using a complex adaptive systems perspective. Decision Sciences, 38(4): 547-580. https://doi.org/10.1111/j.1540- 5915.2007.00170.x
• Sahebjamnia N., Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., 2018. Sustainable tire closed-loop supply chain network design: hybrid metaheuristic algorithms for large-scale networks. Journal of Cleaner Production, 196: 273-296. https://doi.org/10.1016/j.jclepro.2018.05.24 5
• Scholten K., Schilder S., 2015. The role of collaboration in supply chain resilience. Supply Chain Management, 20(4): 471-484. https://doi.org/10.1108/SCM-11-2014-0386
• Seiler A., Papanagnou C., Scarf P., 2020. On the relationship between financial performance and position of businesses in supply chain networks. International Journal of Production Economics, 227. https://doi.org/10.1016/j.ijpe.2020.107690
• Seuring S., 2013. A review of modeling approaches for sustainable supply chain management. Decision Support Systems, 54(4): 1513-1520. https://doi.org/10.1016/j.dss.2012.05.053
• Son B. G., Chae S., Kocabasoglu-Hillmer C., 2021. Catastrophic supply chain disruptions and supply network changes a study of the 2011 Japanese earthquake. International Journal of Operations & Production Management, 41(6): 781-804. https://doi.org/10.1108/IJOPM-09-2020- 0614
• Surana A., Kumara S., Greaves M., Raghavan U. N., 2005. Supply-chain networks: a complex adaptive systems perspective. International Journal of Production Research, 43(20): 4235-4265. https://doi.org/10.1080/0020754050014227 4
• Wang H., Yan X., Guo H., 2019. Visualizing the knowledge domain of embodied language cognition: A bibliometric review. Digital Scholarship in the Humanities, 34(1): 21-31. https://doi.org/10.1093/llc/fqy010
• Wichmann B.K., Kaufmann L., 2016. Social network analysis in supply chain management research. International Journal of Physical Distribution & Logistics Management, 46(8): 740-762. https://doi.org/1Q.11Q8/IJPDLM-Q5-2Q15- 0122
• Wilhelm M.M., Blome C., Bhakoo V., Paulraj A., 2016. Sustainability in multi-tier supply chains: Understanding the double agency role of the first-tier supplier. Journal of Operations Management, 41(1): 42-60. https://doi.org/1Q.1Q16/j.jom.2Q15.11.QQ1

Identyfikatory

DOI

10.17270/J.LOG.2022.676